Abstract

In this paper, a novel algorithm for the accurate detection of QRS complex by combining the independent detection of R and S peaks, using fusion algorithm is proposed. R peak detection has been extensively studied and is being used to detect the QRS complex. Whereas, S peaks, which is also part of QRS complex can be independently detected to aid the detection of QRS complex. In this paper, we suggest a method to first estimate S peak from raw ECG signal and then use them to aid the detection of QRS complex. The amplitude of S peak in ECG signal is relatively weak than corresponding R peak, which is traditionally used for the detection of QRS complex, therefore, an appropriate digital filter is designed to enhance the S peaks. These enhanced S peaks are then detected by adaptive thresholding. The algorithm is validated on all the signals of MIT-BIH arrhythmia database and noise stress database taken from physionet.org. The algorithm performs reasonably well even for the signals highly corrupted by noise. The algorithm performance is confirmed by sensitivity and positive predictivity of 99.99% and the detection accuracy of 99.98% for QRS complex detection. The number of false positives and false negatives resulted while analysis has been drastically reduced to 80 and 42 against the 98 and 84 the best results reported so far.

Keywords

QRS complex S peak Wavelet transform Fusion algorithm

Associate Editors Robert L. Abraham and Ajit P. Yoganathan oversaw the review of this article

Notes

Acknowledgments

This study was funded by Government of India, Ministry of Science and Technology, Department of Science and Technology, (Grant Number : SR/WOS-A/ET-1049/2015(G)).

Conflict of interest

Pooja Sabherwal has received research grants from Department of Science and Technology, India. Dr Latika Singh declares that she has no conflict of interest. Dr Monika Agrawal declares that she has no conflict of interest.

Ethical Approval

This article does not contain any studies with animals and humans performed by any of the authors. All the analysis of the algorithm has been done on the freely available data from physionet.org.26

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